基于深度学习的JUNO实验$^{14}$C堆叠识别
Deep Learning-Based $^{14}$C Pile-Up Identification in the JUNO Experiment
AI总结：
本研究利用深度学习模型识别JUNO实验中$^{14}$C堆叠事件，通过多种模型提升堆叠事件识别精度，为中微子质量顺序测量提供技术支持。
摘要：
测量中微子质量顺序（NMO）是中微子物理中的根本挑战。为解决这一问题，江门地下中微子观测站（JUNO）实验计划于2024年底开始数据采集，目标是在6年内以3σ置信水平确定NMO。实现这一目标的关键在于确保正电子的能量分辨率高质量。然而，液态闪烁体中残留的$^{14}$C同位素会引入堆叠效应，影响正电子能量分辨率。减轻这些堆叠效应需要识别堆叠事件，这提出了重大挑战。$^{14}$C的信号远小于正电子信号，使其识别困难。此外，正电子与$^{14}$C事件时间及顶点的接近进一步加剧了识别难度。本贡献聚焦于深度学习模型在$^{14}$C堆叠事件识别中的应用。涵盖了多种模型，包括基于卷积的模型和先进的Transformer模型。通过性能评估，显示基于深度学习的方法在识别堆叠事件方面具有潜力。
英文摘要：
Measuring neutrino mass ordering (NMO) poses a fundamental challenge in neutrino physics. To address this, the Jiangmen Underground Neutrino Observatory (JUNO) experiment is scheduled to commence data collection in late 2024, with the ambitious goal of determining the NMO at a 3-sigma confidence level within a span of 6 years. A key factor in achieving this is ensuring a high-quality energy resolution of positrons. However, the presence of residual $^{14}$C isotopes in the liquid scintillator introduces pile-up effects that can impact the positron energy resolution. Mitigating these pile-up effects requires the identification of pile-up events, which presents a significant challenge. The signal from $^{14}$C is considerably smaller compared to the positron signal, making its identification difficult. Additionally, the close event time and vertex between a positron and a $^{14}$C further compound the identification challenge. This contribution focuses on the application of deep learning models for the identification of $^{14}$C pile-up events. It encompasses a range of models, including convolution-based models and advanced transformer models. Through performance evaluation, it shows the deep learning-based methods is promising to identify the pile-up events.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
